A Bayesian Network Approach to Assess and Predict Software Quality Using Activity-Based Quality Models

TL;DR

This paper presents a systematic four-step method to derive Bayesian networks from activity-based quality models for assessing and predicting software quality, demonstrated with NASA and open source data.

Contribution

It introduces a novel approach to operationalize activity-based quality models using Bayesian networks for software quality assessment and prediction.

Findings

The approach is applicable to real-world data from NASA and open source projects.

Predictive accuracy depends on data quality and distribution.

Further case studies are needed to validate predictive validity.

Abstract

Context: Software quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty. Objective: The qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model. Method: The four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed. Results: The approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions. Conclusion: The approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.